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基于修正似然滤波的无人机编队相对导航方法

苏炳志 王磊 张红伟 汪海涵 石璐璐

苏炳志,王磊,张红伟,等. 基于修正似然滤波的无人机编队相对导航方法[J]. 北京航空航天大学学报,2023,49(3):569-579 doi: 10.13700/j.bh.1001-5965.2021.0313
引用本文: 苏炳志,王磊,张红伟,等. 基于修正似然滤波的无人机编队相对导航方法[J]. 北京航空航天大学学报,2023,49(3):569-579 doi: 10.13700/j.bh.1001-5965.2021.0313
SU B Z,WANG L,ZHANG H W,et al. Relative navigation method based on modified likelihood filtering for unmanned aerial vehicle formation[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(3):569-579 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0313
Citation: SU B Z,WANG L,ZHANG H W,et al. Relative navigation method based on modified likelihood filtering for unmanned aerial vehicle formation[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(3):569-579 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0313

基于修正似然滤波的无人机编队相对导航方法

doi: 10.13700/j.bh.1001-5965.2021.0313
详细信息
    通讯作者:

    E-mail:subingzhi_hit@163.com

  • 中图分类号: V249.3

Relative navigation method based on modified likelihood filtering for unmanned aerial vehicle formation

More Information
  • 摘要:

    针对无人机编队相对导航系统中视觉导航传感器量测数据存在随机时延问题,提出一种能够处理多步随机延迟量测的修正似然容积卡尔曼滤波(ML-CKF)算法。用多个伯努利随机变量对量测模型进行修正以描述随机延迟;通过边缘化延迟变量来计算滤波的似然函数以从延迟量测中提取准确的信息;采用三阶球面-径向容积准则计算高斯加权积分以解决系统的非线性。滤波中的加权因子根据接收量测的特性进行调整,因此,所提修正似然滤波具有自适应卡尔曼滤波属性。利用罗德里格斯参数表示姿态误差,设计了基于修正似然容积卡尔曼滤波的相对导航滤波器。仿真结果表明:所提算法可以准确地估计出长机和僚机之间的相对位置、速度和姿态,且估计精度高于容积卡尔曼滤波和传统随机时延滤波。

     

  • 图 1  修正似然容积卡尔曼滤波流程

    Figure 1.  Flowchart of modified likelihood cubature Kalman filtering

    图 2  视觉传感器量测

    Figure 2.  Measurement of visual sensor

    图 3  长机和僚机飞行轨迹

    Figure 3.  Flight path of leader and follower

    图 4  相对位置估计误差

    Figure 4.  Estimation error of relative position

    图 5  相对速度估计误差

    Figure 5.  Estimation error of relative velocity

    图 6  相对姿态估计误差

    Figure 6.  Estimation error of relative attitude

    图 7  相对位置估计精度对比

    Figure 7.  Comparison of estimation accuracies of relative positions

    图 8  相对速度估计精度对比

    Figure 8.  Comparison of estimation accuracies of relative velocities

    图 9  相对姿态估计精度对比

    Figure 9.  Comparison of estimation accuracies of relative attitudes

    表  1  惯性和视觉传感器偏差参数

    Table  1.   Deviation parameter of inertial and visual sensors

    参数数值
    加速度计初始漂移/$ {\rm{mg}} $$0.2\;$
    加速度计随机游走/(${\rm{mg}}\cdot{{\rm{s}}^{-1/2} }$)$ 0.002\; $
    加速度计噪声/($ \;{\rm{mg}} \cdot {{\rm{s}}^{1/2}} $)$ 0.02 $
    陀螺仪初始漂移/($(^\circ) \cdot {\rm{h} }^{-1}$)$0.1\;$
    陀螺仪随机游走/($( ^\circ) \cdot { {\rm{h} }^{-3/2} }$)$0.06\;$
    陀螺仪噪声/($(^\circ) \cdot{ {\rm{h} }^{-1/2} }$)$0.01$
    视觉传感器噪声/$ \text{µ} {\rm{rad}} $$ 80\; $
    下载: 导出CSV

    表  2  特征光点位置列表

    Table  2.   List of beacon locations

    特征光点标号$ {X_j} $/m$ {Y_j} $/m$ {Z_j} $/m
    11.500
    2−2.500
    302.50
    40−2.50
    503.5−0.5
    60−3.50.5
    下载: 导出CSV

    表  3  不同滤波算法的计算耗时

    Table  3.   Single computation time of different filtering algorithms

    滤波算法计算耗时/ms
    CKF0.60
    ORD-CKF1.88
    MRD-CKF12.72
    ML-CKF5.49
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-08
  • 录用日期:  2021-09-08
  • 网络出版日期:  2021-09-16
  • 整期出版日期:  2023-03-30

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